{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 调n_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read in data，数据在xgboost安装的路径下的demo目录,现在我们将其copy到当前代码下的data目录\n",
    "dpath = './data/'\n",
    "dtrain = xgb.DMatrix(dpath + 'RentListingInquries_FE_train.bin')\n",
    "dtest = xgb.DMatrix(dpath + 'RentListingInquries_FE_test.bin')\n",
    "train = pd.read_csv(dpath + 'RentListingInquries_FE_train.csv')\n",
    "test = pd.read_csv(dpath + 'RentListingInquries_FE_test.csv')\n",
    "\n",
    "\n",
    "\n",
    "# drop ids and get labels\n",
    "y_train = train['interest_level']\n",
    "train = train.drop([\"interest_level\"], axis=1)\n",
    "X_train = np.array(train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# prepare cross validation\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认参数，此时学习率为0.1，比较大，观察弱分类数目的大致范围 （采用默认参数配置，看看模型是过拟合还是欠拟合）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#直接调用xgboost内嵌的交叉验证（cv），可对连续的n_estimators参数进行快速交叉验证\n",
    "#而GridSearchCV只能对有限个参数进行交叉验证\n",
    "def modelfit(alg, X_train, y_train, cv_folds=None, early_stopping_rounds=10):\n",
    "    xgb_param = alg.get_xgb_params()\n",
    "    xgb_param['num_class'] = 3\n",
    "    \n",
    "    #直接调用xgboost，而非sklarn的wrapper类\n",
    "    xgtrain = xgb.DMatrix(X_train, label = y_train)\n",
    "        \n",
    "    cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], folds =cv_folds,\n",
    "             metrics='mlogloss', early_stopping_rounds=early_stopping_rounds)\n",
    "  \n",
    "    cvresult.to_csv('1_nestimators.csv', index_label = 'n_estimators')\n",
    "    \n",
    "    #最佳参数n_estimators\n",
    "    n_estimators = cvresult.shape[0]\n",
    "    \n",
    "    # 采用交叉验证得到的最佳参数n_estimators，训练模型\n",
    "    alg.set_params(n_estimators = n_estimators)\n",
    "    alg.fit(X_train, y_train, eval_metric='mlogloss')\n",
    "        \n",
    "    #Predict training set:\n",
    "    #train_predprob = alg.predict_proba(X_train)\n",
    "    #logloss = log_loss(y_train, train_predprob)\n",
    "\n",
    "   #Print model report:\n",
    "   # print (\"logloss of train :\" )\n",
    "   # print logloss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#params = {\"objective\": \"multi:softprob\", \"eval_metric\":\"mlogloss\", \"num_class\": 9}\n",
    "xgb1 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=1000,  #数值大没关系，cv会自动返回合适的n_estimators\n",
    "        max_depth=5,\n",
    "        min_child_weight=1,\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel=0.7,\n",
    "        objective= 'multi:softprob',\n",
    "        seed=3)\n",
    "\n",
    "modelfit(xgb1, X_train, y_train, cv_folds = kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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xcJbNK6WqNE5tbemMr4/pMhu+G9PF6mKoXKkPC4BZ8kEmU0ke3fUkt67/PR4e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\nGBNSFgDGGBNSFgDGGBNS/x8iIz7gBDCP0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd1d3eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvresult = pd.DataFrame.from_csv('1_nestimators.csv')\n",
    "        \n",
    "# plot\n",
    "test_means = cvresult['test-mlogloss-mean']\n",
    "test_stds = cvresult['test-mlogloss-std'] \n",
    "        \n",
    "train_means = cvresult['train-mlogloss-mean']\n",
    "train_stds = cvresult['train-mlogloss-std'] \n",
    "\n",
    "x_axis = range(0, cvresult.shape[0])\n",
    "        \n",
    "pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "pyplot.xlabel( 'n_estimators' )\n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig( 'n_estimators4_1.png' )\n",
    "pyplot.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xRwAAAIh1ZKYQU7zT/nqzdmlc1gjddPl8/eLRzZKkg+X1uvelHf3Sp6LSWrJU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\nSZIkSVIBBlOSJEmSVIDBlCRJkiQV8L+cyaDarzaZhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd1d3f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvresult = pd.DataFrame.from_csv('1_nestimators.csv')\n",
    "\n",
    "cvresult = cvresult.iloc[40:]\n",
    "# plot\n",
    "test_means = cvresult['test-mlogloss-mean']\n",
    "test_stds = cvresult['test-mlogloss-std'] \n",
    "        \n",
    "train_means = cvresult['train-mlogloss-mean']\n",
    "train_stds = cvresult['train-mlogloss-std'] \n",
    "\n",
    "x_axis = range(40,cvresult.shape[0]+40)\n",
    "        \n",
    "fig = pyplot.figure(figsize=(10, 10), dpi=100)\n",
    "pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "pyplot.xlabel( 'n_estimators' )\n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig( 'n_estimators_detail.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "227"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb1.n_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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